Categorical Data Type¶

Categoricals can only take on a limited number of possible values (categories) and
can be sorted according to the custom order of the categories.

To harness Categorical data type in Lets-Plot you can either add a pandas.Categotical variable to
your pandas.DataFrame or annotate any variable in your dataset as Categorical using
Lets-Plot as_discrete() function and the levels parameter.

In [1]:

import pandas as pd

from lets_plot import *
from lets_plot.mapping import as_discrete

In [2]:

LetsPlot.setup_html()

In [3]:

mpg_df = pd.read_csv ("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
mpg_df.head(4)

Out[3]:

	Unnamed: 0	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	3	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	4	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact

1. Data Type of the "manufacturer" is `Unordered Discrete` by Default .¶

In [4]:

ggplot(mpg_df) + geom_bar(aes(x='manufacturer')) + coord_flip()

Out[4]:

In [5]:

# 
# Create a list of categories sorted according to a num. of vehicles in the dataset.
# 

brands_by_count = mpg_df['manufacturer'].value_counts().index.tolist()
brands_by_count

Out[5]:

['dodge',
 'toyota',
 'volkswagen',
 'ford',
 'chevrolet',
 'audi',
 'hyundai',
 'subaru',
 'nissan',
 'honda',
 'jeep',
 'pontiac',
 'land rover',
 'mercury',
 'lincoln']

2. First Option: Add a `pandas.Categorical` Variable¶

In [6]:

manufacturer_cat = pd.Categorical(mpg_df['manufacturer'], categories=brands_by_count, ordered=True)
mpg_df['manufacturer_cat'] = manufacturer_cat

In [7]:

ggplot(mpg_df) + \
    geom_bar(aes(x='manufacturer_cat'),
             labels=layer_labels(['..count..']).format('..count..', 'd'),
             tooltips='none') + \
    coord_flip()

Out[7]:

3. Second Option: Annotate "manufacturer" as a `Categorical` Using `as_discrete(levels=..)`¶

In [8]:

ggplot(mpg_df) + \
    geom_bar(aes(x=as_discrete('manufacturer', levels=brands_by_count)),
             labels=layer_labels(['..count..']).format('..count..', 'd'),
             tooltips='none') + \
    coord_flip()

Out[8]:

In [9]:

ggplot(mpg_df) + \
    geom_pie(aes(fill='drv', size='..sum..')) + \
    facet_wrap(facets='manufacturer_cat', ncol=5, order=0) + \
    scale_size(range=[2, 10]) + \
    guides(size='none') + \
    theme_void()

Out[9]:

Categorical Data Type¶

1. Data Type of the "manufacturer" is Unordered Discrete by Default .¶

2. First Option: Add a pandas.Categorical Variable¶

3. Second Option: Annotate "manufacturer" as a Categorical Using as_discrete(levels=..)¶

4. Faceted Plots Respect the Order of Categories in Facet Variable¶

1. Data Type of the "manufacturer" is `Unordered Discrete` by Default .¶

2. First Option: Add a `pandas.Categorical` Variable¶

3. Second Option: Annotate "manufacturer" as a `Categorical` Using `as_discrete(levels=..)`¶